论文标题
基数登记的霍克斯养蜂模型
Cardinality-Regularized Hawkes-Granger Model
论文作者
论文摘要
我们为时间事件数据提出了一个新的稀疏Granger-Causal学习框架。我们专注于称为Hawkes流程的特定点过程。我们首先指出，霍克斯过程的大多数现有稀疏因果学习算法在最大似然估计中都具有奇异性。结果，它们的稀疏解决方案只能显示为数值伪像。在本文中，我们提出了一个基于基于基数调查的鹰队过程的数学定义明确的稀疏因果学习框架，该过程可以补救现有方法的病理问题。我们利用所提出的算法来完成实例因果事件分析的任务，其中稀疏性起着至关重要的作用。我们使用两个真实用例验证了所提出的框架，一个来自电网，另一个来自云数据中心管理域。
We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing out that most of the existing sparse causal learning algorithms for the Hawkes process suffer from a singularity in maximum likelihood estimation. As a result, their sparse solutions can appear only as numerical artifacts. In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches. We leverage the proposed algorithm for the task of instance-wise causal event analysis, where sparsity plays a critical role. We validate the proposed framework with two real use-cases, one from the power grid and the other from the cloud data center management domain.
